Our de-occlusion and removal framework can be applied to any off-the-shelf pose estimators. However, designing a more powerful hand pose estimation network architecture is not the focus of this paper. In this work, we choose the DetNet of MinimalHand [1]} as our baseline SHPE for its simplicity and good performance. MinimalHand [1]} comprises two modules, i.e. DetNet and IKNet. DetNet predicts the 2D and 3D hand joint positions. IKNet then takes as input the predicted 3D hand joint positions and maps them to the joint angles. In our implementation, we simply discard the IKNet and re-train the DetNet on the InterHand2.6M dataset [3]}.
The loss function of SHPE is as follows:
\(\begin{aligned}\mathcal {L}_{SHPE}= \mathcal {L}_{heat} + \mathcal {L}_{loc} + \mathcal {L}_{delta}+ \mathcal {L}_{reg},\end{aligned}\) 
